arXiv AI
Verifiable abstention makes AI leak diagnosis accountable in urban water distribution networks
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arXiv AI
Aug 20
Verifiable abstention makes AI leak diagnosis accountable in water distribution networks
The paper proposes a verifiable abstention framework for AI-driven leak localization in water distribution networks, where a physics-based executor agent tests leak hypotheses against a digital twin and an independent supervisor with an LLM auditor certifies actions or abstains. In noisy field conditions, the system achieves 96% decision precision on acted events, correctly identifies all leaks in a benchmark, and demonstrates practical deployment with a 194-event audit record. The approach offers a defensible, accountable method for autonomous water‑infrastructure operation.
arXiv Machine Learning
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